* All homework can be done in any language. Most are either in the R programming language or in Scilab/Matlab and Python.

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* If you do the homework in two different languages, you get double the homework grade (bonus of 100%)

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=== Assignment 1 ===

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* Exercise 1 Ch1 of the Pattern Theory book by Mumford & Desolneux,

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''Simulating Discrete Random Variables'' (pp 51, 52, 53)

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* Type your solutions in Latex

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* Due date: 24Mai18

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* No need to read this book for this exercise. You will review discrete random variables in the context of Markov chains for natural language processing; this is basic for most AI bots nowadays. If you're curious about the applications, you can read the book chapter just for fun.

== Exams ==

== Exams ==

Edição das 10h37min de 10 de maio de 2018

This is the main page of an undergraduate-level course in stochastic processes targeted at engineering students (mainly computer engineering and its interface with mechanical engineering), being taught in 2018 semester 1 at the Polytechnic Institute IPRJ/UERJ.

Pre-requisites

Desirable: Intermediate programming experience with any numerics scripting language such as Scilab, Python, R or Matlab. Knowing at least one of them will help you learn any new language needed in the course.

Software

The R programming language and data exploration environment will be used for learning, with others used occasionally. The student can also choose to do his homework in Python, Scilab, Matlab or similar languages. The R language has received growing attention, specially in the past couple of years, but it is simple enough so that the student can adapt the code to his preferred language. Students are expected to learn any of these languages on their own as needed, by doing tutorials and asking questions

Approximate Content

This year's course will focus on a modern approach bridging theory and practice.
As engineers and scientists, you should not learn theory here without also considering broader applications. Recent applications in artificial intelligence, machine learning, robotics, autonomous driving, material science and other topics will be considered. These applications are often too hard to tackle at the level of this course, but having contact with them will help motivate the abstract theory. We will try to focus on key concepts and more realistic applications than most courses (that come from the 1900's), that will prompt us to elaborate theory.

Pattern Theory: The Stochastic Analysis of Real-World Signals, David Mumford and Agnes Desolneux - the first chapters already cover many types of stochastic processes in text, signal and image AI

My own machine learning and computational modeling book draft, co-written with prof. Francisco Duarte Moura Neto and focused on diffusion processes on graphs like PageRank. There is a probability chapter which is the basis for this course. We have many copies at IPRJ's library.

Other books to look at

Basic probability and statistics

I recommend you review from the above books. They all include a review. But you might have to see:

Elementary Statistics, Mario Triola (passed down to me by a great scientist and statistician)

Interesting books

R for data science, O'Reilly (#1 data science bestseller on Amazon)

Machine Learning

Pattern Theory: From Representation to Inference, Ulf Grenader

Lectures

Lectures roughly follow the sequence of our main book, with some additional material as needed. All necessary background will be covered as needed.

Overview & Course Logistics (10Mai18)

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Partial listing

Homework

All homework can be done in any language. Most are either in the R programming language or in Scilab/Matlab and Python.

If you do the homework in two different languages, you get double the homework grade (bonus of 100%)

Assignment 1

Exercise 1 Ch1 of the Pattern Theory book by Mumford & Desolneux,

Simulating Discrete Random Variables (pp 51, 52, 53)

Type your solutions in Latex

Due date: 24Mai18

No need to read this book for this exercise. You will review discrete random variables in the context of Markov chains for natural language processing; this is basic for most AI bots nowadays. If you're curious about the applications, you can read the book chapter just for fun.